Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations127
Missing cells99
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.3 KiB
Average record size in memory252.0 B

Variable types

Categorical17
Numeric8
Text2
DateTime1
Boolean27

Alerts

chimney has constant value "0" Constant
obtention_date has constant value "2019-03-29 00:00:00" Constant
condition_promoción de obra nueva has constant value "False" Constant
condition_segunda mano/para reformar has constant value "False" Constant
heating_calefacción individual: bomba de frío/calor has constant value "False" Constant
heating_no dispone de calefacción has constant value "False" Constant
orientation_norte, sur, este, oeste has constant value "False" Constant
orientation_norte, sur, oeste has constant value "False" Constant
ad_last_update is highly overall correlated with energetic_certif and 3 other fieldsHigh correlation
air_conditioner is highly overall correlated with house_type and 1 other fieldsHigh correlation
bath_num is highly overall correlated with loc_city and 6 other fieldsHigh correlation
condition_segunda mano/buen estado is highly overall correlated with floorHigh correlation
construct_date is highly overall correlated with heating_calefacción central: gasoil and 3 other fieldsHigh correlation
energetic_certif is highly overall correlated with ad_last_update and 4 other fieldsHigh correlation
floor is highly overall correlated with condition_segunda mano/buen estado and 1 other fieldsHigh correlation
heating_calefacción central: gasoil is highly overall correlated with ad_last_update and 1 other fieldsHigh correlation
heating_calefacción individual: eléctrica is highly overall correlated with energetic_certifHigh correlation
heating_calefacción individual: gas propano/butano is highly overall correlated with ad_last_update and 3 other fieldsHigh correlation
house_type is highly overall correlated with air_conditioner and 3 other fieldsHigh correlation
loc_city is highly overall correlated with bath_num and 9 other fieldsHigh correlation
loc_district is highly overall correlated with bath_num and 11 other fieldsHigh correlation
loc_zone is highly overall correlated with loc_city and 2 other fieldsHigh correlation
m2_real is highly overall correlated with bath_num and 5 other fieldsHigh correlation
m2_useful is highly overall correlated with ad_last_update and 7 other fieldsHigh correlation
orientation_norte is highly overall correlated with loc_cityHigh correlation
orientation_norte, este is highly overall correlated with construct_dateHigh correlation
orientation_norte, oeste is highly overall correlated with construct_date and 1 other fieldsHigh correlation
orientation_norte, sur, este is highly overall correlated with loc_city and 2 other fieldsHigh correlation
orientation_sur, oeste is highly overall correlated with construct_dateHigh correlation
price is highly overall correlated with air_conditioner and 3 other fieldsHigh correlation
room_num is highly overall correlated with bath_num and 5 other fieldsHigh correlation
swimming_pool is highly overall correlated with bath_num and 4 other fieldsHigh correlation
air_conditioner is highly imbalanced (79.8%) Imbalance
lift is highly imbalanced (54.9%) Imbalance
loc_city is highly imbalanced (62.5%) Imbalance
loc_zone is highly imbalanced (56.9%) Imbalance
reduced_mobility is highly imbalanced (57.5%) Imbalance
swimming_pool is highly imbalanced (79.8%) Imbalance
condition_segunda mano/buen estado is highly imbalanced (72.5%) Imbalance
heating_calefacción central is highly imbalanced (63.1%) Imbalance
heating_calefacción central: gas is highly imbalanced (79.8%) Imbalance
heating_calefacción central: gasoil is highly imbalanced (83.9%) Imbalance
heating_calefacción individual is highly imbalanced (52.4%) Imbalance
heating_calefacción individual: eléctrica is highly imbalanced (88.3%) Imbalance
heating_calefacción individual: gas propano/butano is highly imbalanced (93.4%) Imbalance
orientation_este is highly imbalanced (79.8%) Imbalance
orientation_este, oeste is highly imbalanced (54.9%) Imbalance
orientation_norte is highly imbalanced (76.1%) Imbalance
orientation_norte, este is highly imbalanced (93.4%) Imbalance
orientation_norte, este, oeste is highly imbalanced (88.3%) Imbalance
orientation_norte, oeste is highly imbalanced (93.4%) Imbalance
orientation_norte, sur is highly imbalanced (69.2%) Imbalance
orientation_norte, sur, este is highly imbalanced (93.4%) Imbalance
orientation_oeste is highly imbalanced (60.2%) Imbalance
orientation_sur is highly imbalanced (52.4%) Imbalance
orientation_sur, este is highly imbalanced (79.8%) Imbalance
orientation_sur, este, oeste is highly imbalanced (88.3%) Imbalance
orientation_sur, oeste is highly imbalanced (88.3%) Imbalance
energetic_certif has 41 (32.3%) missing values Missing
floor has 14 (11.0%) missing values Missing
loc_district has 6 (4.7%) missing values Missing
loc_neigh has 38 (29.9%) missing values Missing
house_id has unique values Unique
room_num has 2 (1.6%) zeros Zeros

Reproduction

Analysis started2025-08-03 13:07:48.101937
Analysis finished2025-08-03 13:08:08.232312
Duration20.13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ad_last_update
Categorical

High correlation 

Distinct47
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Anuncio actualizado el 27 de marzo
15 
Anuncio actualizado el 25 de marzo
12 
Anuncio actualizado el 24 de marzo
Anuncio actualizado el 28 de marzo
 
7
Anuncio actualizado el 21 de marzo
 
7
Other values (42)
78 

Length

Max length38
Median length34
Mean length34.149606
Min length29

Characters and Unicode

Total characters4337
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)19.7%

Sample

1st rowAnuncio actualizado el 29 de marzo
2nd rowmás de 2 meses sin actualizar
3rd rowmás de 2 meses sin actualizar
4th rowmás de 2 meses sin actualizar
5th rowAnuncio actualizado el 21 de marzo

Common Values

ValueCountFrequency (%)
Anuncio actualizado el 27 de marzo 15
 
11.8%
Anuncio actualizado el 25 de marzo 12
 
9.4%
Anuncio actualizado el 24 de marzo 8
 
6.3%
Anuncio actualizado el 28 de marzo 7
 
5.5%
Anuncio actualizado el 21 de marzo 7
 
5.5%
Anuncio actualizado el 29 de marzo 5
 
3.9%
Anuncio actualizado el 11 de marzo 5
 
3.9%
Anuncio actualizado el 18 de marzo 5
 
3.9%
Anuncio actualizado el 20 de marzo 4
 
3.1%
Anuncio actualizado el 12 de marzo 4
 
3.1%
Other values (37) 55
43.3%

Length

2025-08-03T13:08:08.340260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 127
16.7%
anuncio 124
16.3%
actualizado 124
16.3%
el 124
16.3%
marzo 97
12.7%
27 15
 
2.0%
febrero 14
 
1.8%
25 12
 
1.6%
24 9
 
1.2%
26 9
 
1.2%
Other values (31) 107
14.0%

Most occurring characters

ValueCountFrequency (%)
635
14.6%
a 478
11.0%
o 370
 
8.5%
e 306
 
7.1%
i 262
 
6.0%
n 260
 
6.0%
u 254
 
5.9%
c 254
 
5.9%
d 253
 
5.8%
l 252
 
5.8%
Other values (21) 1013
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
635
14.6%
a 478
11.0%
o 370
 
8.5%
e 306
 
7.1%
i 262
 
6.0%
n 260
 
6.0%
u 254
 
5.9%
c 254
 
5.9%
d 253
 
5.8%
l 252
 
5.8%
Other values (21) 1013
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
635
14.6%
a 478
11.0%
o 370
 
8.5%
e 306
 
7.1%
i 262
 
6.0%
n 260
 
6.0%
u 254
 
5.9%
c 254
 
5.9%
d 253
 
5.8%
l 252
 
5.8%
Other values (21) 1013
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
635
14.6%
a 478
11.0%
o 370
 
8.5%
e 306
 
7.1%
i 262
 
6.0%
n 260
 
6.0%
u 254
 
5.9%
c 254
 
5.9%
d 253
 
5.8%
l 252
 
5.8%
Other values (21) 1013
23.4%

air_conditioner
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
123 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Length

2025-08-03T13:08:08.454025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:08.539043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

balcony
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
111 
1
16 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

Length

2025-08-03T13:08:08.633934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:08.733832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 111
87.4%
1 16
 
12.6%

bath_num
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1023622
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:08.818399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum38
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.33532
Coefficient of variation (CV)1.5864631
Kurtosis108.65367
Mean2.1023622
Median Absolute Deviation (MAD)1
Skewness10.069652
Sum267
Variance11.124359
MonotonicityNot monotonic
2025-08-03T13:08:08.920712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 62
48.8%
1 49
38.6%
3 8
 
6.3%
4 4
 
3.1%
5 2
 
1.6%
38 1
 
0.8%
6 1
 
0.8%
ValueCountFrequency (%)
1 49
38.6%
2 62
48.8%
3 8
 
6.3%
4 4
 
3.1%
5 2
 
1.6%
6 1
 
0.8%
38 1
 
0.8%
ValueCountFrequency (%)
38 1
 
0.8%
6 1
 
0.8%
5 2
 
1.6%
4 4
 
3.1%
3 8
 
6.3%
2 62
48.8%
1 49
38.6%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
69 
1
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

Length

2025-08-03T13:08:09.032322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:09.104601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

Most occurring characters

ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69
54.3%
1 58
45.7%

chimney
Categorical

Constant 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
127 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 127
100.0%

Length

2025-08-03T13:08:09.194717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:09.262321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 127
100.0%

Most occurring characters

ValueCountFrequency (%)
0 127
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 127
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 127
100.0%

construct_date
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992.3307
Minimum1960
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:09.326757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1977.9
Q11993
median1993
Q31993
95-th percentile1998.5
Maximum2015
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.5283936
Coefficient of variation (CV)0.003276762
Kurtosis9.5985991
Mean1992.3307
Median Absolute Deviation (MAD)0
Skewness-1.6739193
Sum253026
Variance42.619923
MonotonicityNot monotonic
2025-08-03T13:08:09.429648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1993 108
85.0%
1985 2
 
1.6%
2007 2
 
1.6%
1976 2
 
1.6%
1965 1
 
0.8%
2015 1
 
0.8%
1974 1
 
0.8%
1977 1
 
0.8%
2000 1
 
0.8%
1970 1
 
0.8%
Other values (7) 7
 
5.5%
ValueCountFrequency (%)
1960 1
 
0.8%
1965 1
 
0.8%
1970 1
 
0.8%
1974 1
 
0.8%
1976 2
 
1.6%
1977 1
 
0.8%
1980 1
 
0.8%
1985 2
 
1.6%
1990 1
 
0.8%
1993 108
85.0%
ValueCountFrequency (%)
2015 1
 
0.8%
2010 1
 
0.8%
2008 1
 
0.8%
2007 2
 
1.6%
2002 1
 
0.8%
2000 1
 
0.8%
1995 1
 
0.8%
1993 108
85.0%
1990 1
 
0.8%
1985 2
 
1.6%

energetic_certif
Categorical

High correlation  Missing 

Distinct3
Distinct (%)3.5%
Missing41
Missing (%)32.3%
Memory size1.1 KiB
en trámite
47 
no indicado
38 
inmueble exento
 
1

Length

Max length15
Median length10
Mean length10.5
Min length10

Characters and Unicode

Total characters903
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowno indicado
2nd rowno indicado
3rd rowno indicado
4th rowno indicado
5th rowno indicado

Common Values

ValueCountFrequency (%)
en trámite 47
37.0%
no indicado 38
29.9%
inmueble exento 1
 
0.8%
(Missing) 41
32.3%

Length

2025-08-03T13:08:09.562869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:09.649225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
en 47
27.3%
trámite 47
27.3%
no 38
22.1%
indicado 38
22.1%
inmueble 1
 
0.6%
exento 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 125
13.8%
i 124
13.7%
e 98
10.9%
t 95
10.5%
86
9.5%
o 77
8.5%
d 76
8.4%
m 48
 
5.3%
r 47
 
5.2%
á 47
 
5.2%
Other values (6) 80
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 903
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 125
13.8%
i 124
13.7%
e 98
10.9%
t 95
10.5%
86
9.5%
o 77
8.5%
d 76
8.4%
m 48
 
5.3%
r 47
 
5.2%
á 47
 
5.2%
Other values (6) 80
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 903
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 125
13.8%
i 124
13.7%
e 98
10.9%
t 95
10.5%
86
9.5%
o 77
8.5%
d 76
8.4%
m 48
 
5.3%
r 47
 
5.2%
á 47
 
5.2%
Other values (6) 80
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 903
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 125
13.8%
i 124
13.7%
e 98
10.9%
t 95
10.5%
86
9.5%
o 77
8.5%
d 76
8.4%
m 48
 
5.3%
r 47
 
5.2%
á 47
 
5.2%
Other values (6) 80
8.9%

floor
Categorical

High correlation  Missing 

Distinct19
Distinct (%)16.8%
Missing14
Missing (%)11.0%
Memory size1.1 KiB
planta 4ª exterior
17 
planta 5ª exterior
12 
planta 1ª exterior
12 
planta 2ª exterior
12 
planta 3ª exterior
10 
Other values (14)
50 

Length

Max length18
Median length18
Mean length15.619469
Min length8

Characters and Unicode

Total characters1765
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st rowplanta 5ª exterior
2nd rowplanta 1ª exterior
3rd rowplanta 1ª exterior
4th rowplanta 1ª exterior
5th rowbajo exterior

Common Values

ValueCountFrequency (%)
planta 4ª exterior 17
13.4%
planta 5ª exterior 12
9.4%
planta 1ª exterior 12
9.4%
planta 2ª exterior 12
9.4%
planta 3ª exterior 10
7.9%
bajo exterior 8
 
6.3%
2 plantas 6
 
4.7%
planta 7ª exterior 5
 
3.9%
planta 6ª exterior 5
 
3.9%
1 planta 4
 
3.1%
Other values (9) 22
17.3%
(Missing) 14
11.0%

Length

2025-08-03T13:08:09.797177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
planta 94
30.7%
exterior 85
27.8%
20
 
6.5%
16
 
5.2%
14
 
4.6%
13
 
4.2%
12
 
3.9%
plantas 11
 
3.6%
bajo 8
 
2.6%
2 6
 
2.0%
Other values (7) 27
 
8.8%

Most occurring characters

ValueCountFrequency (%)
a 218
12.4%
193
10.9%
t 193
10.9%
r 176
10.0%
e 173
9.8%
n 108
 
6.1%
p 105
 
5.9%
l 105
 
5.9%
o 96
 
5.4%
i 91
 
5.2%
Other values (13) 307
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 218
12.4%
193
10.9%
t 193
10.9%
r 176
10.0%
e 173
9.8%
n 108
 
6.1%
p 105
 
5.9%
l 105
 
5.9%
o 96
 
5.4%
i 91
 
5.2%
Other values (13) 307
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 218
12.4%
193
10.9%
t 193
10.9%
r 176
10.0%
e 173
9.8%
n 108
 
6.1%
p 105
 
5.9%
l 105
 
5.9%
o 96
 
5.4%
i 91
 
5.2%
Other values (13) 307
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 218
12.4%
193
10.9%
t 193
10.9%
r 176
10.0%
e 173
9.8%
n 108
 
6.1%
p 105
 
5.9%
l 105
 
5.9%
o 96
 
5.4%
i 91
 
5.2%
Other values (13) 307
17.4%

garage
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
82 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

Length

2025-08-03T13:08:09.928839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:10.022310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

Most occurring characters

ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 82
64.6%
1 45
35.4%

garden
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
100 
1
27 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

Length

2025-08-03T13:08:10.108907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:10.177427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 100
78.7%
1 27
 
21.3%

house_id
Real number (ℝ)

Unique 

Distinct127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71843026
Minimum1811730
Maximum84861203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:10.284303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1811730
5-th percentile26900758
Q181764170
median84180864
Q384675660
95-th percentile84823347
Maximum84861203
Range83049473
Interquartile range (IQR)2911490

Descriptive statistics

Standard deviation23330339
Coefficient of variation (CV)0.32474049
Kurtosis0.73552664
Mean71843026
Median Absolute Deviation (MAD)605250
Skewness-1.5094721
Sum9.1240643 × 109
Variance5.4430472 × 1014
MonotonicityNot monotonic
2025-08-03T13:08:10.437357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84861203 1
 
0.8%
83306410 1
 
0.8%
83537586 1
 
0.8%
26011638 1
 
0.8%
83624854 1
 
0.8%
84139748 1
 
0.8%
84100618 1
 
0.8%
84737072 1
 
0.8%
84248636 1
 
0.8%
84684858 1
 
0.8%
Other values (117) 117
92.1%
ValueCountFrequency (%)
1811730 1
0.8%
1942576 1
0.8%
2121147 1
0.8%
26011638 1
0.8%
26328217 1
0.8%
26554757 1
0.8%
26699231 1
0.8%
27370989 1
0.8%
27519479 1
0.8%
29523967 1
0.8%
ValueCountFrequency (%)
84861203 1
0.8%
84855714 1
0.8%
84852132 1
0.8%
84851233 1
0.8%
84849625 1
0.8%
84835440 1
0.8%
84823984 1
0.8%
84821862 1
0.8%
84819370 1
0.8%
84818546 1
0.8%

house_type
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.062992
Minimum15
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:10.548885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115
median15
Q316
95-th percentile22
Maximum24
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1921506
Coefficient of variation (CV)0.13647212
Kurtosis3.962424
Mean16.062992
Median Absolute Deviation (MAD)0
Skewness2.2148385
Sum2040
Variance4.8055243
MonotonicityNot monotonic
2025-08-03T13:08:10.635813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15 92
72.4%
16 8
 
6.3%
18 8
 
6.3%
22 6
 
4.7%
17 5
 
3.9%
19 2
 
1.6%
24 2
 
1.6%
21 2
 
1.6%
20 1
 
0.8%
23 1
 
0.8%
ValueCountFrequency (%)
15 92
72.4%
16 8
 
6.3%
17 5
 
3.9%
18 8
 
6.3%
19 2
 
1.6%
20 1
 
0.8%
21 2
 
1.6%
22 6
 
4.7%
23 1
 
0.8%
24 2
 
1.6%
ValueCountFrequency (%)
24 2
 
1.6%
23 1
 
0.8%
22 6
 
4.7%
21 2
 
1.6%
20 1
 
0.8%
19 2
 
1.6%
18 8
 
6.3%
17 5
 
3.9%
16 8
 
6.3%
15 92
72.4%

lift
Categorical

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
115 
0.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters381
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 115
90.6%
0.0 12
 
9.4%

Length

2025-08-03T13:08:10.748694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:10.841893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 115
90.6%
0.0 12
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 139
36.5%
. 127
33.3%
1 115
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 139
36.5%
. 127
33.3%
1 115
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 139
36.5%
. 127
33.3%
1 115
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 139
36.5%
. 127
33.3%
1 115
30.2%

loc_city
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Vitoria-Gasteiz
100 
Iruña de Oca
 
5
Alegria-Dulantzi
 
4
Artziniega
 
3
Ribera Baja
 
2
Other values (12)
13 

Length

Max length19
Median length15
Mean length14.062992
Min length4

Characters and Unicode

Total characters1786
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)8.7%

Sample

1st rowVitoria-Gasteiz
2nd rowVitoria-Gasteiz
3rd rowVitoria-Gasteiz
4th rowLabastida
5th rowVitoria-Gasteiz

Common Values

ValueCountFrequency (%)
Vitoria-Gasteiz 100
78.7%
Iruña de Oca 5
 
3.9%
Alegria-Dulantzi 4
 
3.1%
Artziniega 3
 
2.4%
Ribera Baja 2
 
1.6%
Zigoitia 2
 
1.6%
Okondo 1
 
0.8%
Villabuena de Álava 1
 
0.8%
Labastida 1
 
0.8%
Barrundia 1
 
0.8%
Other values (7) 7
 
5.5%

Length

2025-08-03T13:08:10.946347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vitoria-gasteiz 100
70.4%
de 6
 
4.2%
iruña 5
 
3.5%
oca 5
 
3.5%
alegria-dulantzi 4
 
2.8%
artziniega 3
 
2.1%
ribera 3
 
2.1%
baja 2
 
1.4%
zigoitia 2
 
1.4%
okondo 1
 
0.7%
Other values (11) 11
 
7.7%

Most occurring characters

ValueCountFrequency (%)
i 330
18.5%
a 244
13.7%
t 212
11.9%
e 121
 
6.8%
r 118
 
6.6%
o 109
 
6.1%
z 107
 
6.0%
- 104
 
5.8%
V 102
 
5.7%
s 101
 
5.7%
Other values (24) 238
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 330
18.5%
a 244
13.7%
t 212
11.9%
e 121
 
6.8%
r 118
 
6.6%
o 109
 
6.1%
z 107
 
6.0%
- 104
 
5.8%
V 102
 
5.7%
s 101
 
5.7%
Other values (24) 238
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 330
18.5%
a 244
13.7%
t 212
11.9%
e 121
 
6.8%
r 118
 
6.6%
o 109
 
6.1%
z 107
 
6.0%
- 104
 
5.8%
V 102
 
5.7%
s 101
 
5.7%
Other values (24) 238
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 330
18.5%
a 244
13.7%
t 212
11.9%
e 121
 
6.8%
r 118
 
6.6%
o 109
 
6.1%
z 107
 
6.0%
- 104
 
5.8%
V 102
 
5.7%
s 101
 
5.7%
Other values (24) 238
13.3%

loc_district
Categorical

High correlation  Missing 

Distinct42
Distinct (%)34.7%
Missing6
Missing (%)4.7%
Memory size1.1 KiB
Distrito Centro
17 
Distrito Lovaina - Aranzabal
15 
Distrito Casco Viejo
13 
Distrito Armentia - Ciudad Jardín
Distrito Zabalgana - Ariznabarra
Other values (37)
61 

Length

Max length34
Median length31
Mean length22.272727
Min length6

Characters and Unicode

Total characters2695
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)22.3%

Sample

1st rowDistrito Aranzabela - Aranbizkarra
2nd rowDistrito Adurtza
3rd rowDistrito Lovaina - Aranzabal
4th rowCalle Marrate, 2
5th rowDistrito Armentia - Ciudad Jardín

Common Values

ValueCountFrequency (%)
Distrito Centro 17
13.4%
Distrito Lovaina - Aranzabal 15
 
11.8%
Distrito Casco Viejo 13
 
10.2%
Distrito Armentia - Ciudad Jardín 8
 
6.3%
Distrito Zabalgana - Ariznabarra 7
 
5.5%
Distrito San Martín 5
 
3.9%
Distrito Coronación 5
 
3.9%
Distrito Aranzabela - Aranbizkarra 5
 
3.9%
Distrito Judimendi - Sta.Lucía 4
 
3.1%
Distrito Salburua 4
 
3.1%
Other values (32) 38
29.9%
(Missing) 6
 
4.7%

Length

2025-08-03T13:08:11.088807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
distrito 100
25.8%
45
 
11.6%
centro 17
 
4.4%
lovaina 15
 
3.9%
aranzabal 15
 
3.9%
casco 13
 
3.4%
viejo 13
 
3.4%
calle 9
 
2.3%
armentia 8
 
2.1%
jardín 8
 
2.1%
Other values (71) 144
37.2%

Most occurring characters

ValueCountFrequency (%)
a 310
11.5%
i 294
10.9%
266
 
9.9%
t 260
 
9.6%
r 252
 
9.4%
o 196
 
7.3%
n 132
 
4.9%
s 124
 
4.6%
D 101
 
3.7%
e 84
 
3.1%
Other values (47) 676
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 310
11.5%
i 294
10.9%
266
 
9.9%
t 260
 
9.6%
r 252
 
9.4%
o 196
 
7.3%
n 132
 
4.9%
s 124
 
4.6%
D 101
 
3.7%
e 84
 
3.1%
Other values (47) 676
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 310
11.5%
i 294
10.9%
266
 
9.9%
t 260
 
9.6%
r 252
 
9.4%
o 196
 
7.3%
n 132
 
4.9%
s 124
 
4.6%
D 101
 
3.7%
e 84
 
3.1%
Other values (47) 676
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 310
11.5%
i 294
10.9%
266
 
9.9%
t 260
 
9.6%
r 252
 
9.4%
o 196
 
7.3%
n 132
 
4.9%
s 124
 
4.6%
D 101
 
3.7%
e 84
 
3.1%
Other values (47) 676
25.1%
Distinct119
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:11.467667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length133
Median length82
Mean length67.984252
Min length29

Characters and Unicode

Total characters8634
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)89.0%

Sample

1st row extremadura , Distrito Aranzabela - Aranbizkarra , Vitoria-Gasteiz , Álava
2nd row Calle Heraclio Fournier, 33 , Distrito Adurtza , Vitoria-Gasteiz , Álava
3rd row Avenida Gasteiz, 50 , Distrito Lovaina - Aranzabal , Vitoria-Gasteiz , Álava
4th row Calle Marrate, 2 , Labastida , Laguardia-Rioja Alavesa, Álava
5th row Calle Maite Zuñiga, 2 , Distrito Armentia - Ciudad Jardín , Vitoria-Gasteiz , Álava
ValueCountFrequency (%)
404
29.9%
álava 128
 
9.5%
vitoria-gasteiz 106
 
7.8%
distrito 100
 
7.4%
calle 59
 
4.4%
de 22
 
1.6%
centro 17
 
1.3%
aranzabal 15
 
1.1%
lovaina 15
 
1.1%
casco 14
 
1.0%
Other values (231) 473
35.0%
2025-08-03T13:08:12.052911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1480
17.1%
a 1134
13.1%
i 756
 
8.8%
t 540
 
6.3%
r 484
 
5.6%
, 456
 
5.3%
o 392
 
4.5%
l 391
 
4.5%
e 389
 
4.5%
s 271
 
3.1%
Other values (59) 2341
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1480
17.1%
a 1134
13.1%
i 756
 
8.8%
t 540
 
6.3%
r 484
 
5.6%
, 456
 
5.3%
o 392
 
4.5%
l 391
 
4.5%
e 389
 
4.5%
s 271
 
3.1%
Other values (59) 2341
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1480
17.1%
a 1134
13.1%
i 756
 
8.8%
t 540
 
6.3%
r 484
 
5.6%
, 456
 
5.3%
o 392
 
4.5%
l 391
 
4.5%
e 389
 
4.5%
s 271
 
3.1%
Other values (59) 2341
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1480
17.1%
a 1134
13.1%
i 756
 
8.8%
t 540
 
6.3%
r 484
 
5.6%
, 456
 
5.3%
o 392
 
4.5%
l 391
 
4.5%
e 389
 
4.5%
s 271
 
3.1%
Other values (59) 2341
27.1%

loc_neigh
Text

Missing 

Distinct85
Distinct (%)95.5%
Missing38
Missing (%)29.9%
Memory size1.1 KiB
2025-08-03T13:08:12.377633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length29
Mean length20.011236
Min length5

Characters and Unicode

Total characters1781
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)91.0%

Sample

1st rowextremadura
2nd rowCalle Heraclio Fournier, 33
3rd rowAvenida Gasteiz, 50
4th rowCalle Maite Zuñiga, 2
5th rowGaztelako Atea, 54
ValueCountFrequency (%)
calle 45
 
15.9%
de 13
 
4.6%
avenida 6
 
2.1%
atea 6
 
2.1%
urb 5
 
1.8%
la 5
 
1.8%
gaztelako 5
 
1.8%
gasteiz 5
 
1.8%
vitoria-gasteiz 5
 
1.8%
paseo 4
 
1.4%
Other values (136) 184
65.0%
2025-08-03T13:08:12.847785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 239
13.4%
194
 
10.9%
e 162
 
9.1%
l 147
 
8.3%
i 112
 
6.3%
r 98
 
5.5%
o 73
 
4.1%
n 63
 
3.5%
t 57
 
3.2%
d 54
 
3.0%
Other values (55) 582
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 239
13.4%
194
 
10.9%
e 162
 
9.1%
l 147
 
8.3%
i 112
 
6.3%
r 98
 
5.5%
o 73
 
4.1%
n 63
 
3.5%
t 57
 
3.2%
d 54
 
3.0%
Other values (55) 582
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 239
13.4%
194
 
10.9%
e 162
 
9.1%
l 147
 
8.3%
i 112
 
6.3%
r 98
 
5.5%
o 73
 
4.1%
n 63
 
3.5%
t 57
 
3.2%
d 54
 
3.0%
Other values (55) 582
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 239
13.4%
194
 
10.9%
e 162
 
9.1%
l 147
 
8.3%
i 112
 
6.3%
r 98
 
5.5%
o 73
 
4.1%
n 63
 
3.5%
t 57
 
3.2%
d 54
 
3.0%
Other values (55) 582
32.7%

loc_zone
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Álava
101 
Añana, Álava
 
9
Ayala, Álava
 
5
Salvatierra, Álava
 
5
Laguardia-Rioja Alavesa, Álava
 
3
Other values (2)
 
4

Length

Max length31
Median length5
Mean length7.2204724
Min length5

Characters and Unicode

Total characters917
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowÁlava
2nd rowÁlava
3rd rowÁlava
4th rowLaguardia-Rioja Alavesa, Álava
5th rowÁlava

Common Values

ValueCountFrequency (%)
Álava 101
79.5%
Añana, Álava 9
 
7.1%
Ayala, Álava 5
 
3.9%
Salvatierra, Álava 5
 
3.9%
Laguardia-Rioja Alavesa, Álava 3
 
2.4%
Zuya, Álava 3
 
2.4%
Campezo- Montaña Alavesa, Álava 1
 
0.8%

Length

2025-08-03T13:08:12.991310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:13.087137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
álava 127
80.4%
añana 9
 
5.7%
ayala 5
 
3.2%
salvatierra 5
 
3.2%
alavesa 4
 
2.5%
laguardia-rioja 3
 
1.9%
zuya 3
 
1.9%
campezo 1
 
0.6%
montaña 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 323
35.2%
l 141
15.4%
v 136
14.8%
Á 127
 
13.8%
31
 
3.4%
, 26
 
2.8%
A 18
 
2.0%
r 13
 
1.4%
i 11
 
1.2%
ñ 10
 
1.1%
Other values (20) 81
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 323
35.2%
l 141
15.4%
v 136
14.8%
Á 127
 
13.8%
31
 
3.4%
, 26
 
2.8%
A 18
 
2.0%
r 13
 
1.4%
i 11
 
1.2%
ñ 10
 
1.1%
Other values (20) 81
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 323
35.2%
l 141
15.4%
v 136
14.8%
Á 127
 
13.8%
31
 
3.4%
, 26
 
2.8%
A 18
 
2.0%
r 13
 
1.4%
i 11
 
1.2%
ñ 10
 
1.1%
Other values (20) 81
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 323
35.2%
l 141
15.4%
v 136
14.8%
Á 127
 
13.8%
31
 
3.4%
, 26
 
2.8%
A 18
 
2.0%
r 13
 
1.4%
i 11
 
1.2%
ñ 10
 
1.1%
Other values (20) 81
 
8.8%

m2_real
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.6063
Minimum35
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:13.229115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile50.3
Q175
median90
Q3120
95-th percentile458
Maximum2000
Range1965
Interquartile range (IQR)45

Descriptive statistics

Standard deviation310.1548
Coefficient of variation (CV)1.7763094
Kurtosis21.123576
Mean174.6063
Median Absolute Deviation (MAD)20
Skewness4.5362054
Sum22175
Variance96196.002
MonotonicityNot monotonic
2025-08-03T13:08:13.375795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 10
 
7.9%
80 10
 
7.9%
90 10
 
7.9%
75 8
 
6.3%
85 8
 
6.3%
95 6
 
4.7%
70 6
 
4.7%
200 3
 
2.4%
65 3
 
2.4%
280 3
 
2.4%
Other values (46) 60
47.2%
ValueCountFrequency (%)
35 1
0.8%
42 2
1.6%
43 2
1.6%
50 2
1.6%
51 1
0.8%
52 2
1.6%
56 1
0.8%
60 2
1.6%
63 1
0.8%
64 1
0.8%
ValueCountFrequency (%)
2000 1
 
0.8%
1900 1
 
0.8%
1500 2
1.6%
1000 1
 
0.8%
900 1
 
0.8%
500 1
 
0.8%
360 1
 
0.8%
300 1
 
0.8%
292 1
 
0.8%
280 3
2.4%

m2_useful
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.44094
Minimum39
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:13.553396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile58.6
Q180
median87
Q390
95-th percentile201
Maximum2000
Range1961
Interquartile range (IQR)10

Descriptive statistics

Standard deviation174.5682
Coefficient of variation (CV)1.5950904
Kurtosis111.48183
Mean109.44094
Median Absolute Deviation (MAD)5
Skewness10.280099
Sum13899
Variance30474.058
MonotonicityNot monotonic
2025-08-03T13:08:13.692312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
87 53
41.7%
90 7
 
5.5%
80 6
 
4.7%
70 6
 
4.7%
60 4
 
3.1%
110 3
 
2.4%
72 3
 
2.4%
68 3
 
2.4%
65 2
 
1.6%
75 2
 
1.6%
Other values (30) 38
29.9%
ValueCountFrequency (%)
39 2
 
1.6%
41 1
 
0.8%
50 2
 
1.6%
55 1
 
0.8%
58 1
 
0.8%
60 4
3.1%
65 2
 
1.6%
68 3
2.4%
70 6
4.7%
72 3
2.4%
ValueCountFrequency (%)
2000 1
0.8%
350 1
0.8%
300 1
0.8%
250 1
0.8%
248 1
0.8%
220 1
0.8%
210 1
0.8%
180 1
0.8%
150 1
0.8%
132 1
0.8%

obtention_date
Date

Constant 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2019-03-29 00:00:00
Maximum2019-03-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-03T13:08:13.804807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:13.889487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

price
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1015.2126
Minimum350
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:14.569702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile550
Q1717.5
median850
Q31100
95-th percentile2000
Maximum5000
Range4650
Interquartile range (IQR)382.5

Descriptive statistics

Standard deviation596.29983
Coefficient of variation (CV)0.58736449
Kurtosis17.438291
Mean1015.2126
Median Absolute Deviation (MAD)150
Skewness3.5570871
Sum128932
Variance355573.49
MonotonicityNot monotonic
2025-08-03T13:08:14.812587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750 10
 
7.9%
650 8
 
6.3%
850 7
 
5.5%
700 6
 
4.7%
1200 5
 
3.9%
900 5
 
3.9%
990 5
 
3.9%
1100 4
 
3.1%
800 4
 
3.1%
780 3
 
2.4%
Other values (47) 70
55.1%
ValueCountFrequency (%)
350 1
 
0.8%
360 1
 
0.8%
400 1
 
0.8%
520 1
 
0.8%
525 1
 
0.8%
550 3
 
2.4%
595 1
 
0.8%
600 2
 
1.6%
640 1
 
0.8%
650 8
6.3%
ValueCountFrequency (%)
5000 1
 
0.8%
3000 3
2.4%
2500 1
 
0.8%
2400 1
 
0.8%
2000 2
1.6%
1800 2
1.6%
1600 1
 
0.8%
1500 3
2.4%
1400 3
2.4%
1375 1
 
0.8%

reduced_mobility
Categorical

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
116 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

Length

2025-08-03T13:08:15.007535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:15.118594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116
91.3%
1 11
 
8.7%

room_num
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9370079
Minimum0
Maximum35
Zeros2
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-08-03T13:08:15.214403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4.7
Maximum35
Range35
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.0595882
Coefficient of variation (CV)1.0417365
Kurtosis97.370334
Mean2.9370079
Median Absolute Deviation (MAD)1
Skewness9.2515408
Sum373
Variance9.3610799
MonotonicityNot monotonic
2025-08-03T13:08:15.351488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 57
44.9%
2 30
23.6%
1 16
 
12.6%
4 15
 
11.8%
5 5
 
3.9%
0 2
 
1.6%
6 1
 
0.8%
35 1
 
0.8%
ValueCountFrequency (%)
0 2
 
1.6%
1 16
 
12.6%
2 30
23.6%
3 57
44.9%
4 15
 
11.8%
5 5
 
3.9%
6 1
 
0.8%
35 1
 
0.8%
ValueCountFrequency (%)
35 1
 
0.8%
6 1
 
0.8%
5 5
 
3.9%
4 15
 
11.8%
3 57
44.9%
2 30
23.6%
1 16
 
12.6%
0 2
 
1.6%

storage_room
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
75 
1
52 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

Length

2025-08-03T13:08:15.564529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:15.661248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

Most occurring characters

ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75
59.1%
1 52
40.9%

swimming_pool
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
123 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Length

2025-08-03T13:08:15.806808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:15.908694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123
96.9%
1 4
 
3.1%

terrace
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
80 
1
47 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%

Length

2025-08-03T13:08:16.036514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:16.173960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%

Most occurring characters

ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80
63.0%
1 47
37.0%
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:16.245527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

condition_segunda mano/buen estado
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
True
121 
False
 
6
ValueCountFrequency (%)
True 121
95.3%
False 6
 
4.7%
2025-08-03T13:08:16.310699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:16.381564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating_calefacción central
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
118 
True
 
9
ValueCountFrequency (%)
False 118
92.9%
True 9
 
7.1%
2025-08-03T13:08:16.452610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
123 
True
 
4
ValueCountFrequency (%)
False 123
96.9%
True 4
 
3.1%
2025-08-03T13:08:16.536413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating_calefacción central: gasoil
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
124 
True
 
3
ValueCountFrequency (%)
False 124
97.6%
True 3
 
2.4%
2025-08-03T13:08:16.618756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
114 
True
13 
ValueCountFrequency (%)
False 114
89.8%
True 13
 
10.2%
2025-08-03T13:08:16.698437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:16.786019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating_calefacción individual: eléctrica
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
125 
True
 
2
ValueCountFrequency (%)
False 125
98.4%
True 2
 
1.6%
2025-08-03T13:08:16.849077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
109 
True
18 
ValueCountFrequency (%)
False 109
85.8%
True 18
 
14.2%
2025-08-03T13:08:16.932661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating_calefacción individual: gas propano/butano
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
126 
True
 
1
ValueCountFrequency (%)
False 126
99.2%
True 1
 
0.8%
2025-08-03T13:08:17.019196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:17.088517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_este
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
123 
True
 
4
ValueCountFrequency (%)
False 123
96.9%
True 4
 
3.1%
2025-08-03T13:08:17.137648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_este, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
115 
True
12 
ValueCountFrequency (%)
False 115
90.6%
True 12
 
9.4%
2025-08-03T13:08:17.204109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
122 
True
 
5
ValueCountFrequency (%)
False 122
96.1%
True 5
 
3.9%
2025-08-03T13:08:17.253836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, este
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
126 
True
 
1
ValueCountFrequency (%)
False 126
99.2%
True 1
 
0.8%
2025-08-03T13:08:17.304522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
125 
True
 
2
ValueCountFrequency (%)
False 125
98.4%
True 2
 
1.6%
2025-08-03T13:08:17.352648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, oeste
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
126 
True
 
1
ValueCountFrequency (%)
False 126
99.2%
True 1
 
0.8%
2025-08-03T13:08:17.408830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, sur
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
120 
True
 
7
ValueCountFrequency (%)
False 120
94.5%
True 7
 
5.5%
2025-08-03T13:08:17.466882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, sur, este
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
126 
True
 
1
ValueCountFrequency (%)
False 126
99.2%
True 1
 
0.8%
2025-08-03T13:08:17.523560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:17.579249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
127 
ValueCountFrequency (%)
False 127
100.0%
2025-08-03T13:08:17.616927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_oeste
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
117 
True
 
10
ValueCountFrequency (%)
False 117
92.1%
True 10
 
7.9%
2025-08-03T13:08:17.656946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
114 
True
13 
ValueCountFrequency (%)
False 114
89.8%
True 13
 
10.2%
2025-08-03T13:08:17.710682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, este
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
123 
True
 
4
ValueCountFrequency (%)
False 123
96.9%
True 4
 
3.1%
2025-08-03T13:08:17.766813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, este, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
125 
True
 
2
ValueCountFrequency (%)
False 125
98.4%
True 2
 
1.6%
2025-08-03T13:08:17.813291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, oeste
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size259.0 B
False
125 
True
 
2
ValueCountFrequency (%)
False 125
98.4%
True 2
 
1.6%
2025-08-03T13:08:17.860513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-08-03T13:08:05.627722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:57.605305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.512325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.609867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.476806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.491194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.902781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.772745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.753172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:57.739480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.616359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.730942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.583482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.677533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:03.533679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.872887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.867383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:57.865720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.711799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.842212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.688938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.862053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:03.743606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.978504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.979733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:57.960145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.810479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.939535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.793156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.027564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:03.936016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.073504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:06.088799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.056664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.909056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.041718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.906747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.199625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.121376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.177280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:06.215260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.191043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.275256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.152364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.015608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.386382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.293880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.285962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:06.331525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.304121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.404264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.274564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.145648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.566862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.436243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.408101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:06.453376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:58.401867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:07:59.495664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:00.367836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:01.310092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:02.730523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:04.646317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:05.499483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-03T13:08:17.999882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ad_last_updateair_conditionerbalconybath_numbuilt_in_wardrobecondition_segunda mano/buen estadoconstruct_dateenergetic_certiffloorgaragegardenheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanohouse_idhouse_typeliftloc_cityloc_districtloc_zonem2_realm2_usefulorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oestepricereduced_mobilityroom_numstorage_roomswimming_poolterrace
ad_last_update1.0000.0000.0000.1600.1240.0000.3110.5450.4010.0000.3190.0000.0000.6840.0000.0000.1900.8000.2270.3410.0000.3570.1840.1990.4020.5110.4710.0000.4100.0000.4030.0000.2530.3630.0000.3240.0000.0000.0000.3200.0000.2800.0000.4300.227
air_conditioner0.0001.0000.0000.4800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.5040.0000.3500.0000.1710.2800.4800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5050.0000.4830.0000.0370.034
balcony0.0000.0001.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.2060.0000.1230.0000.0000.0000.0000.1030.1910.0950.0000.0000.0000.1350.0000.0000.0000.0470.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
bath_num0.1600.4800.0001.0000.0000.0000.1510.0000.3520.2090.2110.0000.0000.0000.0000.0000.2010.000-0.2990.2930.0000.7930.7350.4330.6810.5520.0000.0000.4240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6190.0580.6280.0000.5540.084
built_in_wardrobe0.1240.0000.0000.0001.0000.0970.1050.0000.0930.1760.0840.0000.0000.0000.0000.0000.0530.0000.2500.1920.0000.1320.2630.1470.1370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1230.0000.0000.1670.2360.1260.1250.0000.044
condition_segunda mano/buen estado0.0000.0000.0000.0000.0971.0000.0000.4221.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2650.0000.0000.0000.0000.0000.0000.0000.098
construct_date0.3110.0000.0000.1510.1050.0001.0000.0000.2170.0000.0000.0000.0000.7310.4720.0000.3030.000-0.1000.2630.0000.2500.3660.2560.0110.0780.0000.1760.2810.6540.0000.6540.4430.0000.1370.0000.0000.0000.6550.0110.085-0.0720.1090.0000.000
energetic_certif0.5450.0000.0000.0000.0000.4220.0001.0000.2590.0000.2810.0000.0000.0000.0001.0000.0001.0000.1740.6770.2350.0000.5170.0000.1440.3880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.3410.0000.1210.0250.0000.000
floor0.4010.0000.0570.3520.0931.0000.2170.2591.0000.2530.3050.2140.0000.0000.0860.0000.4230.0000.0000.3260.2940.1120.1930.0000.4200.6530.2850.0000.1130.0000.0000.0000.3440.0000.0000.1660.0000.0000.0000.1650.0000.3580.0000.2640.312
garage0.0000.0000.0000.2090.1760.0900.0000.0000.2531.0000.2650.0620.0500.0000.0520.0000.0000.0000.0000.4350.1270.2210.2690.1560.3740.1470.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4020.1240.2490.2200.0000.253
garden0.3190.0000.0000.2110.0840.0000.0000.2810.3050.2651.0000.0000.0000.0000.0000.0000.0240.0000.1900.4110.0000.2740.3950.1930.2970.2670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2930.1980.2240.0000.2790.287
heating_calefacción central0.0000.0000.0000.0000.0000.0000.0000.0000.2140.0620.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.1040.0000.000
heating_calefacción central: gas0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0001.0000.0000.0000.0000.0000.0000.2480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1360.0000.0000.0000.1940.1630.0000.0540.0000.034
heating_calefacción central: gasoil0.6840.0000.0000.0000.0000.0000.7310.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
heating_calefacción individual0.0000.0000.2060.0000.0000.0000.4720.0000.0860.0520.0000.0000.0000.0001.0000.0000.0460.0000.0450.0000.0000.2910.3640.3190.0360.0000.0000.0000.0000.0760.0000.0760.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.1430.0000.018
heating_calefacción individual: eléctrica0.0000.1310.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
heating_calefacción individual: gas natural0.1900.0000.1230.2010.0530.0000.3030.0000.4230.0000.0240.0000.0000.0000.0460.0001.0000.0000.0000.0000.0000.0000.0000.0000.0340.1400.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0000.0000.000
heating_calefacción individual: gas propano/butano0.8000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.2170.0000.6100.8150.2390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
house_id0.2270.0000.000-0.2990.2500.000-0.1000.1740.0000.0000.1900.0000.2480.0000.0450.0000.0000.0001.000-0.1920.1040.2180.0000.051-0.239-0.2240.2140.0000.0000.0000.2920.0000.0000.0000.0820.2020.0000.2190.000-0.2190.175-0.2450.0000.0000.164
house_type0.3410.5040.0000.2930.1920.1190.2630.6770.3260.4350.4110.0000.0000.0000.0000.0530.0000.217-0.1921.0000.3670.5980.5870.4450.3470.2870.0000.0000.3790.0000.1410.3510.2710.2170.0000.0000.0000.0530.0000.1040.2210.1480.1880.4460.082
lift0.0000.0000.0000.0000.0000.0000.0000.2350.2940.1270.0000.0000.0000.0000.0000.0000.0000.0000.1040.3671.0000.4490.4360.1160.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.062
loc_city0.3570.3500.1030.7930.1320.0000.2500.0000.1120.2210.2740.0000.0000.0000.2910.0000.0000.6100.2180.5980.4491.0000.8670.9570.4480.7250.0000.0000.5190.0000.0000.3440.0000.9380.0000.2120.3500.0000.0000.3360.0000.6730.2080.6700.186
loc_district0.1840.0000.1910.7350.2630.0000.3660.5170.1930.2690.3950.0000.0000.0000.3640.0000.0000.8150.0000.5870.4360.8671.0000.8320.6620.6990.0000.2040.3240.0000.4740.8150.0000.8150.0000.0000.0630.0000.4740.3950.0000.6440.1630.5880.328
loc_zone0.1990.1710.0950.4330.1470.0000.2560.0000.0000.1560.1930.0000.0000.0000.3190.0000.0000.2390.0510.4450.1160.9570.8321.0000.2540.4100.0000.0000.4570.0000.2080.3840.0000.3840.0000.2350.0000.0000.0000.2100.0000.3760.2820.5660.189
m2_real0.4020.2800.0000.6810.1370.0000.0110.1440.4200.3740.2970.0000.0000.0000.0360.0000.0340.000-0.2390.3470.0000.4480.6620.2541.0000.7060.0000.0000.2330.0000.0000.0000.0000.6780.0000.0000.0000.0000.0000.6590.0000.7760.0000.3050.173
m2_useful0.5110.4800.0000.5520.0000.1190.0780.3880.6530.1470.2670.0000.0000.0000.0000.0000.1400.000-0.2240.2870.0000.7250.6990.4100.7061.0000.0000.0000.4240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4020.0000.5910.1080.5340.136
orientation_este0.4710.0000.0000.0000.0000.0000.0000.0000.2850.0000.0000.0000.0000.0000.0000.0000.0000.0000.2140.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.034
orientation_este, oeste0.0000.0000.1350.0000.0000.0000.1760.0000.0000.0420.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0890.0000.2040.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.000
orientation_norte0.4100.0000.0000.4240.0000.0000.2810.0000.1130.0000.0000.0000.0000.3590.0000.0000.0000.0000.0000.3790.0000.5190.3240.4570.2330.4240.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3920.0000.4300.0000.0000.000
orientation_norte, este0.0000.0000.0000.0000.0000.0000.6540.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
orientation_norte, este, oeste0.4030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2920.1410.0000.0000.4740.2080.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.045
orientation_norte, oeste0.0000.0000.0470.0000.0000.0000.6540.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.3510.0000.3440.8150.3840.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
orientation_norte, sur0.2530.0000.0000.0000.0000.0000.4430.0000.3440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.1550.0000.0000.0000.0000.000
orientation_norte, sur, este0.3630.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.2170.0000.9380.8150.3840.6780.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
orientation_oeste0.0000.0000.0000.0000.0000.0000.1370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.063
orientation_sur0.3240.0000.0000.0000.0000.0000.0000.0000.1660.0000.0000.0000.1360.0000.0000.0000.0000.0000.2020.0000.0000.2120.0000.2350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0370.1360.018
orientation_sur, este0.0000.0000.0000.0000.1230.2650.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3500.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.021
orientation_sur, este, oeste0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2190.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.3440.0000.0000.0000.0000.045
orientation_sur, oeste0.0000.0000.0000.0000.0000.0000.6550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
price0.3200.5050.0000.6190.1670.0000.0110.3410.1650.4020.2930.0000.1940.0000.0000.0000.0000.000-0.2190.1040.0000.3360.3950.2100.6590.4020.0000.0000.3920.0000.0000.0000.1550.0000.0000.0000.0000.3440.0001.0000.0000.5550.0000.4660.265
reduced_mobility0.0000.0000.0000.0580.2360.0000.0850.0000.0000.1240.1980.0000.1630.0000.0000.0000.1280.0000.1750.2210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
room_num0.2800.4830.0000.6280.1260.000-0.0720.1210.3580.2490.2240.0100.0000.0000.0000.0000.0000.000-0.2450.1480.0000.6730.6440.3760.7760.5910.0000.0780.4300.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.5550.0001.0000.0000.4830.139
storage_room0.0000.0000.0000.0000.1250.0000.1090.0250.0000.2200.0000.1040.0540.0000.1430.0000.0000.0000.0000.1880.0000.2080.1630.2820.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0001.0000.0000.224
swimming_pool0.4300.0370.0000.5540.0000.0000.0000.0000.2640.0000.2790.0000.0000.0000.0000.0000.0000.0000.0000.4460.0000.6700.5880.5660.3050.5340.0000.0000.0000.0000.0000.0000.0000.0000.0000.1360.0000.0000.0000.4660.0000.4830.0001.0000.034
terrace0.2270.0340.0000.0840.0440.0980.0000.0000.3120.2530.2870.0000.0340.0000.0180.0000.0000.0000.1640.0820.0620.1860.3280.1890.1730.1360.0340.0000.0000.0000.0450.0000.0000.0000.0630.0180.0210.0450.0000.2650.0000.1390.2240.0341.000

Missing values

2025-08-03T13:08:06.804373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-03T13:08:07.275406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-03T13:08:07.704276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ad_last_updateair_conditionerbalconybath_numbuilt_in_wardrobechimneyconstruct_dateenergetic_certiffloorgaragegardenhouse_idhouse_typeliftloc_cityloc_districtloc_fullloc_neighloc_zonem2_realm2_usefulobtention_datepricereduced_mobilityroom_numstorage_roomswimming_poolterracecondition_promoción de obra nuevacondition_segunda mano/buen estadocondition_segunda mano/para reformarheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: bomba de frío/calorheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanoheating_no dispone de calefacciónorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_norte, sur, este, oesteorientation_norte, sur, oesteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oeste
0Anuncio actualizado el 29 de marzo001.00001965.00no indicadoplanta 5ª exterior0084861203151.00Vitoria-GasteizDistrito Aranzabela - Aranbizkarraextremadura , Distrito Aranzabela - Aranbizkarra , Vitoria-Gasteiz , ÁlavaextremaduraÁlava9590.002019-03-2975003.00000FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1más de 2 meses sin actualizar011.00001993.00NaNplanta 1ª exterior0083306410151.00Vitoria-GasteizDistrito AdurtzaCalle Heraclio Fournier, 33 , Distrito Adurtza , Vitoria-Gasteiz , ÁlavaCalle Heraclio Fournier, 33Álava8980.002019-03-2975003.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
2más de 2 meses sin actualizar002.00001993.00no indicadoplanta 1ª exterior0083537586151.00Vitoria-GasteizDistrito Lovaina - AranzabalAvenida Gasteiz, 50 , Distrito Lovaina - Aranzabal , Vitoria-Gasteiz , ÁlavaAvenida Gasteiz, 50Álava12087.002019-03-29100004.00001FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3más de 2 meses sin actualizar002.00001993.00no indicadoplanta 1ª exterior0026011638150.00LabastidaCalle Marrate, 2Calle Marrate, 2 , Labastida , Laguardia-Rioja Alavesa, ÁlavaNaNLaguardia-Rioja Alavesa, Álava120101.002019-03-29120003.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
4Anuncio actualizado el 21 de marzo001.00001993.00no indicadobajo exterior0083624854151.00Vitoria-GasteizDistrito Armentia - Ciudad JardínCalle Maite Zuñiga, 2 , Distrito Armentia - Ciudad Jardín , Vitoria-Gasteiz , ÁlavaCalle Maite Zuñiga, 2Álava5287.002019-03-2970002.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
5Anuncio actualizado el 4 de febrero001.00001993.00no indicadoplanta 1ª exterior0084139748150.00OkondoJandiola Entitatea, 16Jandiola Entitatea, 16 , Okondo , Ayala, ÁlavaNaNAyala, Álava8087.002019-03-2955003.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
6Anuncio actualizado el 31 de enero015.00101993.00NaN4 plantas1184100618161.00Villabuena de ÁlavaCalle Viura, 1Calle Viura, 1 , Villabuena de Álava , Laguardia-Rioja Alavesa, ÁlavaNaNLaguardia-Rioja Alavesa, Álava360300.002019-03-29150014.00111FalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
7Anuncio actualizado el 21 de marzo001.00001993.00NaNbajo exterior0084737072151.00Vitoria-GasteizDistrito Zabalgana - AriznabarraGaztelako Atea, 54 , Distrito Zabalgana - Ariznabarra , Vitoria-Gasteiz , ÁlavaGaztelako Atea, 54Álava4241.002019-03-2965001.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
8Anuncio actualizado el 12 de febrero002.00101993.00en trámiteplanta 6ª exterior0084248636151.00Vitoria-GasteizDistrito LakuaCalle Damaso Alonso, 1 , Urb. Estacion , Distrito Lakua , Vitoria-Gasteiz , ÁlavaUrb. EstacionÁlava8087.002019-03-2980002.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
9Anuncio actualizado el 15 de marzo002.00001993.00en trámiteplanta 2ª0084684858151.00Iruña de OcaPlaza Rita OraáPlaza Rita Oraá , Iruña de Oca , Añana, ÁlavaNaNAñana, Álava9087.002019-03-2965003.00001FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
ad_last_updateair_conditionerbalconybath_numbuilt_in_wardrobechimneyconstruct_dateenergetic_certiffloorgaragegardenhouse_idhouse_typeliftloc_cityloc_districtloc_fullloc_neighloc_zonem2_realm2_usefulobtention_datepricereduced_mobilityroom_numstorage_roomswimming_poolterracecondition_promoción de obra nuevacondition_segunda mano/buen estadocondition_segunda mano/para reformarheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: bomba de frío/calorheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanoheating_no dispone de calefacciónorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_norte, sur, este, oesteorientation_norte, sur, oesteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oeste
117Anuncio actualizado el 25 de marzo001.00001993.00en trámiteplanta 6ª exterior0084794934151.00Vitoria-GasteizDistrito CoronaciónCalle Coronacion de la Virgen Blanca, 1 , Distrito Coronación , Vitoria-Gasteiz , ÁlavaCalle Coronacion de la Virgen Blanca, 1Álava9587.002019-03-2975002.00001FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
118Anuncio actualizado el 25 de marzo001.00101993.00NaNplanta 7ª exterior1139918049221.00Vitoria-GasteizDistrito Zabalgana - AriznabarraGaztelako Atea, 114 , Distrito Zabalgana - Ariznabarra , Vitoria-Gasteiz , ÁlavaGaztelako Atea, 114Álava9087.002019-03-2977411.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
119Anuncio actualizado el 25 de marzo001.00101993.00no indicadoplanta 2ª exterior0084805989151.00Vitoria-GasteizDistrito Casco ViejoCalle Pintore, 30 , Distrito Casco Viejo , Vitoria-Gasteiz , ÁlavaCalle Pintore, 30Álava8465.002019-03-2970002.00100FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
120Anuncio actualizado el 27 de marzo002.00001993.00no indicadoplanta 4ª exterior0033436071151.00Vitoria-GasteizDistrito Lovaina - AranzabalCalle cercas bajas, 11 , Distrito Lovaina - Aranzabal , Vitoria-Gasteiz , ÁlavaCalle cercas bajas, 11Álava12087.002019-03-29120004.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
121Anuncio actualizado el 27 de marzo001.00101993.00en trámiteplanta 2ª exterior1084835440151.00Vitoria-GasteizDistrito Lovaina - AranzabalCercas Bajas , Distrito Lovaina - Aranzabal , Vitoria-Gasteiz , ÁlavaCercas BajasÁlava7570.002019-03-2981002.00000FalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
122Anuncio actualizado el 28 de marzo002.00101993.00en trámiteplanta 7ª exterior0084849625151.00Vitoria-GasteizDistrito Zabalgana - AriznabarraAvenida zabalgana , Distrito Zabalgana - Ariznabarra , Vitoria-Gasteiz , ÁlavaAvenida zabalganaÁlava9590.002019-03-2985003.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
123Anuncio actualizado el 29 de marzo001.00001993.00en trámiteplanta 3ª exterior0084851233151.00Vitoria-GasteizDistrito CoronaciónCalle Bruno Villarreal , Distrito Coronación , Vitoria-Gasteiz , ÁlavaCalle Bruno VillarrealÁlava8776.002019-03-2975002.00001FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
124Anuncio actualizado el 29 de marzo001.00001993.00en trámiteplanta 8ª exterior1184852132151.00Vitoria-GasteizDistrito San MartínCalle Pintor Aurelio Verá-Fajardo , Distrito San Martín , Vitoria-Gasteiz , ÁlavaCalle Pintor Aurelio Verá-FajardoÁlava5187.002019-03-2978001.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
125Anuncio actualizado el 29 de marzo002.00101993.00NaNplanta 5ª exterior1084855714151.00Vitoria-GasteizDistrito CentroCalle Independentzia, 1 , Distrito Centro , Vitoria-Gasteiz , ÁlavaCalle Independentzia, 1Álava9587.002019-03-29130003.00100FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
126Anuncio actualizado el 28 de marzo002.00101993.00no indicadoplanta 3ª exterior011942576151.00Vitoria-GasteizDistrito San MartínAvenida Gasteiz, 93 , Distrito San Martín , Vitoria-Gasteiz , ÁlavaAvenida Gasteiz, 93Álava105100.002019-03-2992503.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse